Field of the Invention
Embodiments of the present invention relate generally to computer-aided design and, more specifically, to deep learning based functional correlation of volumetric designs.
Description of the Related Art
In a conventional engineering workflow, an engineer uses a computer-aided design (CAD) tool to design physical parts that meet certain design criteria. For example, the engineer could design a cantilever capable of supporting a particular load within a building structure. This process can be inefficient, though, because an engineer is only capable of exploring one design option at a time.
To resolve this problem, advanced CAD tools implement a technique referred to as “generative design.” In operation, a generative design CAD tool receives a specification from the engineer that dictates the desired functional attributes associated with a successful design. Then, the generative design tool creates a spectrum of designs having the desired functional attributes.
One problem with generative design tools is that at least one functional attribute is needed as a starting point to generating a spectrum of designs. However, engineers may have difficulty specifying the relevant functional attributes of a successful design, which renders the generative design CAD tool ineffective. For example, an engineer may intuitively understand that a particular load must somehow be supported, yet have difficulty articulating this requirement as one or more functional attributes. Without at least one functional attribute as a starting point, a generative design CAD tool cannot generate a spectrum of designs.
As the foregoing illustrates, what is needed in the art is a more effective approach for specifying functional attributes of potentially successful designs for generative CAD tools.